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Variational problems have proved of value in many image processing and analysis applications. However increase of sensor resolution as occurred in medical imaging and experimental fluid dynamics demand for adapted solving strategies to handle the huge amount of data. In this paper we address the decomposition of the general class of quadratic variational problems, which includes several important...
In particle image velocimetry (PIV) a temporally separated image pair of a gas or liquid seeded with small particles is recorded and analysed in order to measure fluid flows therein. We investigate a variational approach to cross-correlation, a robust and well-established method to determine displacement vectors from the image data. A “soft” Gaussian window function replaces the usual rectangular...
We present a view-based method for steering a robot in a network of positions; this includes navigation along a prerecorded path, but also allows for arbitrary movement of the robot between adjacent positions in the network. The approach uses an upward-looking omnidirectional camera; even a very modest quality of the optical system is sufficient, since all views are represented in terms of low-order...
Dense optical flow fields are required for many applications. They can be obtained by means of various global methods which employ regularization techniques for propagating estimates to regions with insufficient information. However, incorrect flow estimates are propagated as well. We, therefore, propose surface measures for the detection of locations where the full flow can be estimated reliably,...
This article presents a genetic learning algorithm to derive discrete patterns that can be used for classification and retrieval of 3D motion capture data. Based on boolean motion features, the idea is to learn motion class patterns in an evolutionary process with the objective to discriminate a given set of positive from a given set of negative training motions. Here, the fitness of a pattern is...
Motion estimation between two views especially from a minimal set of features is a central problem in computer vision. Although algorithms for computing the motion between two calibrated cameras using point features exist, there is up to now no solution for a scenario employing just circles in space. For this task a minimal and linear solver is presented using only two arbitrary circles. It is shown...
In order to overcome typical problems in markerless motion capture from video, such as ambiguities, noise, and occlusions, many techniques reduce the high dimensional search space by integration of prior information about the movement pattern or scene. In this work, we present an approach in which geometric prior information about the floor location is integrated in the pose tracking process. We penalize...
Virtually all variational methods for motion estimation regularize the gradient of the flow field, which introduces a bias towards piecewise constant motions in weakly textured areas. We propose a novel regularization approach, based on decorrelated second-order derivatives, that does not suffer from this shortcoming. We then derive an efficient numerical scheme to solve the new model using projected...
High-speed image measurements of fluid flows define an important field of research in experimental fluid mechanics and the related industry. Numerous competing methods have been developed for both 2D and 3D measurements. Estimates of fluid flow velocity fields are often corrupted, however, due to various deficiencies of the imaging process, making the physical interpretation of the measurements questionable...
The total variation (TV) measure is a key concept in the field of variational image analysis. Introduced by Rudin, Osher and Fatemi in connection with image denoising, it also provides the basis for convex structure-texture decompositions of image signals, image inpainting, and for globally optimal binary image segmentation by convex functional minimization. Concerning vector-valued image data, the...
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